Signal processing method and processor

ABSTRACT

A first signal of two signals to be compared for similarity is divided into small areas and one small area is selected for calculating the correlation with a second signal using a correlative method. Then, the quantity of translation, expansion rate and similarity in an area where the similarity, which is the square of the correlation value, reaches its maximum, are found. Values based on the similarity are integrated at a position represented by the quantity of translation and expansion rate. Similar processing is performed with respect to all the small areas, and at a peak where the maximum integral value of the similarity is obtained, its magnitude is compared with a threshold value to evaluate the similarity. The small area voted for that peak can be extracted.

TECHNICAL FIELD

[0001] This invention relates to a signal processing method and device,a signal processing program, and a recording medium having a signalprocessing program recorded thereon, and particularly to a signalprocessing method and device, a signal processing program, and arecording medium having a signal processing program recorded thereon forevaluating the similarity between plural signals or between differentsections of one signal.

BACKGROUND ART

[0002] Conventionally, a correlative method is used as a technique forevaluating the similarity of two signals. The correlative method is alsoreferred to as matched filter.

[0003] In this correlative method, correlation of two signals is takenwhile the time between the two signals is shifted, and the similaritycan be evaluated by a correlation value at the time when the maximumcorrelation is taken. The correlative method is an optimum comparisontechnique because it provides the maximum signal-to-noise ratio betweenone signal and the other signal. Particularly when a pattern to bedetected is known, the correlative method is used as a method fordetecting pattern from an observation signal tainted by noise in a widevariety of fields such as signal detection, acoustic processing, imageprocessing, and radar technology.

[0004] Meanwhile, in the case of evaluating the similarity between twoobservation signals from an unknown original signal, or when signals andnoise are unsteady, the correlative method might be dominated byunsteadiness of noise components and cannot necessarily be anappropriate comparison technique. Such a case will now be described indetail.

[0005]FIGS. 1A and 1B show two observation signals A and B includingsimilar signals. The similar signals included in the observation signalshave a shift of 300 samples and a difference in amplitude ofapproximately 1.5 times. The individual observation signals are taintedby unsteady noise signals. In sections indicated by arrows in FIGS. 1Aand 1B, a high signal-to-noise ratio is observed and the two signals arerelatively similar to each other. However, in the other parts, there aremany noise signals and the two signals are hardly similar to each other.As a matter of course, which section has a high signal-to-noise ratio,that is, which section is suitable for similarity evaluation, is notknown in advance.

[0006] Of such observation signals, a part consisting of samples 0 to500 the observation signal A is used as a template and its correlationvalue with the observation signal B is calculated by the correlativemethod. The result is shown in FIG. 1C. As indicated by an arrow in FIG.1C, a peak of correlation is observed near a point where the quantity oftranslation is 300 samples. However, this peak is not significantlylarger than the other peaks and its absolute value is approximately 0.3,which is not high enough. In this manner, with the correlative method,the similarity between observation signals with unsteady signals andnoise as described above cannot be evaluated properly.

DISCLOSURE OF THE INVENTION

[0007] In view of the foregoing status of the art, it is an object ofthe present invention to provide a signal processing method and device,a signal processing program, and a recording medium having a signalprocessing program recorded therein that enable automatic elimination ofa section where a noise component is dominant, extraction of sectionswith high similarity, and evaluation of the similarity using suchsections, even in the case of evaluating the similarity between twoobservation signals from an unknown original signal or when signals andnoise are unsteady.

[0008] In order to achieve the above-described object, a signalprocessing method according to the present invention includes: adivision step of inputting plural signals and dividing at least one ofthe plural signals into plural small areas; a parameter extraction stepof extracting a conversion parameter used for converting the small areasto acquire similarity with the other signal; a totaling step of totalingvalues indicating the degree of similarity found on the basis of theconversion parameter; and a similarity evaluation step of evaluating thesimilarity between the plural signals on the basis of the result of thetotaling.

[0009] The signal processing method may further include a similarsection extraction step of extracting a similar section of the pluralsignals.

[0010] In the signal precessing method, the conversion parameter may befound using a correlative method. In this case, the conversion parameteris, for example, an expansion rate and/or magnitude of a shift at apoint where a maximum correlation value between the small area and theother signal is obtained, and at the totaling step, values indicatingthe degree of similarity between the plural signals are totaled in aspace centering the conversion parameter as an axis.

[0011] In such a signal processing method, at least one of pluralinputted signals is divided into plural small areas and the similaritybetween each small area and the other signal is found. As thesesimilarity values are totaled, the similarity between the plural signalsis evaluated. On the basis of the similarity, a similar section of theplural signals is extracted.

[0012] Moreover, in order to achieve the above-described object, asignal processing method according to the present invention includes: adivision step of inputting plural signals and dividing at least one ofthe plural signals into plural small areas; a parameter extraction stepof extracting a conversion parameter used for converting the small areasto acquire similarity with the other signal; a totaling step of totalingvalues indicating the degree of similarity found on the basis of theconversion parameter; a similarity evaluation step of evaluating thesimilarity between the plural signals on the basis of the result of thetotaling; a similar section extraction step of extracting a similarsection of the plural signals; a first coding step of coding the similarsection of the plural signals extracted at the similar sectionextraction step; and a second coding step of coding the sections otherthan the similar section.

[0013] The conversion is, for example, expansion and/or shiftconversion. In this case, at the first coding step, information of starttime of the similar section, expansion rate, and length of the similarsection is coded.

[0014] In such a signal processing method, at least one of pluralinputted signals is divided into plural small areas and the similaritybetween each small area and the other signal is found. As thesesimilarity values are totaled, the similarity between the plural signalsis evaluated. On the basis of the similarity, a similar section of theplural signals is extracted, and the similar section and the othersections are separately coded.

[0015] Moreover, in order to achieve the above-described object, asignal processing device according to the present invention includes:division means for inputting plural signals and dividing at least one ofthe plural signals into plural small areas; parameter extraction meansfor extracting a conversion parameter used for converting the smallareas to acquire similarity with the other signal; totaling means fortotaling values indicating the degree of similarity found on the basisof the conversion parameter; and similarity evaluation means forevaluating the similarity between the plural signals on the basis of theresult of the totaling.

[0016] The signal processing device may further include similar sectionextraction means for extracting a similar section of the plural signals.

[0017] In the signal processing device, the conversion parameter may befound using a correlative method. In this case, the conversion parameteris, for example, an expansion rate and/or magnitude of a shift at apoint where a maximum correlation value between the small area and theother signal is obtained, and the totaling means totals valuesindicating the degree of similarity between the plural signals in aspace centering the conversion parameter as an axis.

[0018] In such a signal processing device, at least one of pluralinputted signals is divided into plural small areas and the similaritybetween each small area and the other signal is found. As thesesimilarity values are totaled, the similarity between the plural signalsis evaluated. On the basis of the similarity, a similar section of theplural signals is extracted.

[0019] Moreover, in order to achieve the above-described object, asignal processing device according to the present invention includes:division means for inputting plural signals and dividing at least one ofthe plural signals into plural small areas; parameter extraction meansfor extracting a conversion parameter used for converting the smallareas to acquire similarity with the other signal; totaling means fortotaling values indicating the degree of similarity found on the basisof the conversion parameter; similarity evaluation means for evaluatingthe similarity between the plural signals on the basis of the result ofthe totaling; similar section extraction means for extracting a similarsection of the plural signals; first coding means for coding the similarsection of the plural signals extracted by the similar sectionextraction means; and second coding means for coding the sections otherthan the similar section.

[0020] The conversion is, for example, expansion and/or shiftconversion. In this case, the first coding means codes information ofstart time of the similar section, expansion rate, and length of thesimilar section.

[0021] In such a signal processing device, at least one of pluralinputted signals is divided into plural small areas and the similaritybetween each small area and the other signal is found. As thesesimilarity values are totaled, the similarity between the plural signalsis evaluated. On the basis of the similarity, a similar section of theplural signals is extracted, and the similar section and the othersections are separately coded.

[0022] Moreover, in order to achieve the above-described object, asignal processing program according to the present invention includes: adivision step of inputting plural signals and dividing at least one ofthe plural signals into plural small areas; a parameter extraction stepof extracting a conversion parameter used for converting the small areasto acquire similarity with the other signal; a totaling step of totalingvalues indicating the degree of similarity found on the basis of theconversion parameter; and a similarity evaluation step of evaluating thesimilarity between the plural signals on the basis of the result of thetotaling.

[0023] The signal processing program may further include a similarsection extraction step of extracting a similar section of the pluralsignals.

[0024] In the signal processing program, the conversion parameter may befound using a correlative method. In this case, the conversion parameteris, for example, an expansion rate and/or magnitude of a shift at apoint where a maximum correlation value between the small area and theother signal is obtained, and at the totaling step, values indicatingthe degree of similarity between the plural signals are totaled in aspace centering the conversion parameter as an axis.

[0025] In such a signal processing program, at least one of pluralinputted signals is divided into plural small areas and the similaritybetween each small area and the other signal is found. As thesesimilarity values are totaled, the similarity between the plural signalsis evaluated. On the basis of the similarity, a similar section of theplural signals is extracted.

[0026] Moreover, in order to achieve the above-described object, asignal processing program according to the present invention includes: adivision step of inputting plural signals and dividing at least one ofthe plural signals into plural small areas; a parameter extraction stepof extracting a conversion parameter used for converting the small areasto acquire similarity with the other signal; a totaling step of totalingvalues indicating the degree of similarity found on the basis of theconversion parameter; a similarity evaluation step of evaluating thesimilarity between the plural signals on the basis of the result of thetotaling; a similar section extraction step of extracting a similarsection of the plural signals; a first coding step of coding the similarsection of the plural signals extracted at the similar sectionextraction step; and a second coding step of coding the sections otherthan the similar section.

[0027] The conversion is, for example, expansion and/or shiftconversion. In this case, at the first coding step, information of starttime of the similar section, expansion rate, and length of the similarsection is coded.

[0028] In such a signal processing program, at least one of pluralinputted signals is divided into plural small areas and the similaritybetween each small area and the other signal is found. As thesesimilarity values are totaled, the similarity between the plural signalsis evaluated. On the basis of the similarity, a similar section of theplural signals is extracted, and the similar section and the othersections are separately coded.

[0029] Moreover, in order to achieve the above-described object, arecording medium according to the present invention is acomputer-controllable recording medium having a signal processingprogram recorded thereon, the signal processing program including: adivision step of inputting plural signals and dividing at least one ofthe plural signals into plural small areas; a parameter extraction stepof extracting a conversion parameter used for converting the small areasto acquire similarity with the other signal; a totaling step of totalingvalues indicating the degree of similarity found on the basis of theconversion parameter; and a similarity evaluation step of evaluating thesimilarity between the plural signals on the basis of the result of thetotaling.

[0030] The signal processing program may further include a similarsection extraction step of extracting a similar section of the pluralsignals.

[0031] In the signal precessing program, the conversion parameter may befound using a correlative method. In this case, the conversion parameteris, for example, an expansion rate and/or magnitude of a shift at apoint where a maximum correlation value between the small area and theother signal is obtained, and at the totaling step, values indicatingthe degree of similarity between the plural signals are totaled in aspace centering the conversion parameter as an axis.

[0032] In the signal processing program recorded on such a recordingmedium, at least one of plural inputted signals is divided into pluralsmall areas and the similarity between each small area and the othersignal is found. As these similarity values are totaled, the similaritybetween the plural signals is evaluated. On the basis of the similarity,a similar section of the plural signals is extracted.

[0033] Moreover, in order to achieve the above-described object, arecording medium according to the present invention is acomputer-controllable recording medium having a signal processingprogram recorded thereon, the signal processing program including: adivision step of inputting plural signals and dividing at least one ofthe plural signals into plural small areas; a parameter extraction stepof extracting a conversion parameter used for converting the small areasto acquire similarity with the other signal; a totaling step of totalingvalues indicating the degree of similarity found on the basis of theconversion parameter; a similarity evaluation step of evaluating thesimilarity between the plural signals on the basis of the result of thetotaling; a similar section extraction step of extracting a similarsection of the plural signals; a first coding step of coding the similarsection of the plural signals extracted at the similar sectionextraction step; and a second coding step of coding the sections otherthan the similar section.

[0034] The conversion is, for example, expansion and/or shiftconversion. In this case, at the first coding step, information of starttime of the similar section, expansion rate, and length of the similarsection is coded.

[0035] In the signal processing program recorded on such a recordingmedium, at least one of plural inputted signals is divided into pluralsmall areas and the similarity between each small area and the othersignal is found. As these similarity values are totaled, the similaritybetween the plural signals is evaluated. On the basis of the similarity,a similar section of the plural signals is extracted, and the similarsection and the other sections are separately coded.

[0036] The other objects of the present invention and specificadvantages provided by the present invention will be further clarifiedby the following description of an embodiment.

BRIEF DESCRIPTION OF THE DRAWINGS

[0037]FIGS. 1A to 1C are views for explaining exemplary signals suchthat it is difficult to detect similarity by a conventional correlativemethod. FIG. 1A shows the waveform of an observation signal A. FIG. 1Bshows the waveform of an observation signal B. FIG. 1C shows thecorrelation between the observation signal A and the observation signalB found by the correlative method.

[0038]FIGS. 2A to 2C are views for explaining the principle of a signalprocessing method of this embodiment. FIG. 2A shows division of a signalf(x) into small areas. FIG. 2B shows the state where a similar area isdetected in a signal g(x). FIG. 2C shows voting of a parameter of thearea into a voting space.

[0039]FIG. 3 is a view for explaining the principle of the signalprocessing method and showing the state where a peak is formed near apoint with a predetermined quantity of translation and expansion rate.

[0040]FIG. 4 is a view for explaining the principle of the signalprocessing method and showing an example in which similar areas areextracted.

[0041]FIG. 5 is a view for explaining the schematic structure of asignal processing device of this embodiment.

[0042]FIG. 6 is a flowchart for explaining the operation of the signalprocessing device.

[0043]FIG. 7 is a view for explaining the schematic structure of acoding device using the signal processing device.

[0044]FIG. 8 is a flowchart for explaining the operation of the codingdevice.

[0045]FIG. 9 is a view for explaining selection of a first section and asecond section in the coding device.

[0046]FIG. 10 is a view for explaining extraction of a similar sectionin the coding device.

[0047]FIG. 11 is a view for explaining coding of the first section inthe coding device.

[0048]FIG. 12 is a view for explaining another selection of a firstsection and a second section in the coding device.

[0049]FIG. 13 is a view for explaining an exemplary coded signalintegrated in the coding device.

BEST MODE FOR CARRYING OUT THE INVENTION

[0050] A specific embodiment to which the present invention is appliedwill now be described in detail with reference to the drawings. In thisembodiment, the present invention is applied to a signal processingdevice for evaluating the similarity in the case where the same signalcomponent or a similar signal component is included in plural signals orin different sections of one signal and extracting the similar section.Before explaining this signal processing device, the principle of asimilarity evaluation technique in this embodiment will be describedfirst.

[0051] First, if f(x) and g(x) represent two signals to be compared, prepresents a conversion parameter, H_(p)[·] represents a predeterminedconversion group, and n(x) represents a noise component, the two signalscan be expressed by the relation of the following equation (1).

g(x)=H _(p) [f(x)]+n(x)  (1)

[0052] If the result of applying the predetermined conversion H_(p)[·]to the signal f(x) is similar to the signal g(x), the noise componentn(x) is a function with a small value. If the result is not similar tothe signal g(x) at all, the noise component n(x) is a function with alarge value. That is, as the expression of equation (1) is employed, itcan be understood that high similarity between the signal f(x) and thesignal g(x) means that the noise component n(x) is significantly smallwith respect to the signal g(x) for a given conversion parameter p.Considering expansion and translation as the most typical exemplaryconversion, the two signals can be expressed by the relation of thefollowing equation (2). In equation (2), a represents the expansion rateand y represents the quantity of translation.

g(x)=af(x−y)+n(x)  (2)

[0053] When the two signals are expressed by the relation of equation(2), high similarity between the signal f(x) and the signal g(x) meansthat an expansion rate a and a quantity of translation y which realize asignificantly small noise component n(x) exist.

[0054] As is known well, the expansion rate a and the quantity oftranslation y which minimizes the energy of the noise component n(x)with respect to the energy of the signal g(x) can be found by thecorrelative method.

[0055] However, if the noise component n(x) is unsteady and it ispartially weak and partially strong, that is, when the signal g(x)satisfies the following equation (3), the expansion rate a and thequantity of translation y cannot necessarily be found properly by thecorrelative method that uniformly optimizes the whole signal, asdescribed above. $\begin{matrix}{{g(x)} \approx \left\{ \begin{matrix}{{af}\left( {x - y} \right)} & \left( {a\quad {certain}\quad {section}} \right) \\{n(x)} & {\left( {{other}\quad {sections}} \right)\quad}\end{matrix} \right.} & (3)\end{matrix}$

[0056] Thus, in this embodiment, local similarity is found and itsrespective values are integrated to evaluate the similarity as a whole.

[0057] In the technique of this embodiment, first, the signal f(x) isdivided into I units of small sectional signals f_(i)(x) in accordancewith the following equation (4), as shown in FIG. 2A. In equation (4),x_(i) represents the end point of each section and i (=0, 1, . . . ,I-1) indicates the index of each section. Of course, the number ofdivisions is not limited to that shown in FIG. 2A and can be arbitrarilyset. Although the signal is divided so that the individual sections donot overlap each other in FIG. 2A, the sections may overlap each other.$\begin{matrix}{{f_{i}(x)} = \left\{ \begin{matrix}{f(x)} & \left( {x_{i} \leq x < x_{i + 1}} \right) \\0 & ({others})\end{matrix} \right.} & (4)\end{matrix}$

[0058] Next, with respect to each sectional signal f_(i)(x) and thesignal g(x), the expansion rate a=a_(i) and the quantity of translationy=y_(i) that minimize the noise energy J(a, y) for the signal energy,and the similarity s_(i) at that time, are found as in the followingequation (5). The expansion rate a is a multiplication coefficient whichrealizes a pattern of the sectional signal f_(i)(x) that is mostcoincident with the pattern of the signal g(x). $\begin{matrix}{{J\left( {a,y} \right)} = \frac{\int_{x_{i}}^{x_{i + 1}}{{n^{2}\left( {x + y} \right)}{x}}}{\int_{x_{i}}^{x_{i + 1}}{{g^{2}\left( {x + y} \right)}{x}}}} & (5)\end{matrix}$

[0059] This leads to a secondary minimization problem and the quantityof translation y_(i) and the expansion rate a_(i) can be found, asexpressed by the following equations (6) and (7). $\begin{matrix}{y_{i} = {\underset{y}{\arg \quad \max}\left\lbrack \frac{\left\{ {\int_{x_{i}}^{x_{i + 1}}{{f_{i}(x)}{g\left( {x + y} \right)}{x}}} \right\}^{2}}{\int_{x_{i}}^{x_{i + 1}}{{f_{i}^{2}(x)}{x}{\int_{x_{i}}^{x_{i + 1}}{{g^{2}\left( {x + y} \right)}{x}}}}} \right\rbrack}} & (6)\end{matrix}$

$\begin{matrix}{a_{i} = \frac{\int_{x_{i}}^{x_{i + 1}}{{f_{i}(x)}{g\left( {x + y_{i}} \right)}{x}}}{\int_{x_{i}}^{x_{i + 1}}{{f_{i}^{2}(x)}{x}}}} & (7)\end{matrix}$

[0060] Equation (6) means that the quantity of translation y_(i) isfound as the shift quantity that maximizes the correlation (the squareof the correlation) between the sectional signal f_(i)(x) and the signalg(x). Equation (7) indicates that a_(i) is found as the expansion ratethat minimizes the noise energy at that time. For example, as a sectionof the signal g(x) that maximizes the correlation (the square of thecorrelation) between the sectional signal f_(i)(x) and the signal g(x),a section indicated by an arrow in FIG. 2B is found.

[0061] In this case, the similarity s_(i) between the sectional signalf_(i)(x) and the signal g(x) is found as the square of the maximumcorrelation value, as expressed by the following equation (8).$\begin{matrix}{s_{i} = {{1 - {J\left( {a,y} \right)}} = \frac{\left\{ {\int_{x_{i}}^{x_{i + 1}}{{f_{i}(x)}{g\left( {x + y_{i}} \right)}{x}}} \right\}^{2}}{\int_{x_{i}}^{x_{i + 1}}{{f_{i}^{2}(x)}{x}{\int_{x_{i}}^{x_{i + 1}}{{g^{2}\left( {x + y_{i}} \right)}{x}}}}}}} & (8)\end{matrix}$

[0062] Subsequently, after the quantity of translation y_(i), theexpansion rate a_(i) and the similarity s_(i) are found for all thesections i, the similarities of the respective sections are integratedby a voting method. The voting method is such a method that in acharacteristic space h(y,a) where the quantity of translation y and theexpansion rate a are partitioned into appropriate cells, the similaritys_(i) is integrated to the cell to which the quantity of translationy_(i) and the expansion rate a_(i) correspond and the respectivesimilarities are totaled as shown in FIG. 2C, as expressed by thefollowing equation (9). It is equivalent to a kind of histogrampreparation. In equation (9), δ(y,a) is Kronecker's δ, which expresses afunction equal to 1 for y≈y_(i), a≈a_(i), and 0 otherwise. The entireright side is divided by I in order to standardize the integral value at[0,1], independent of the total number of votes, that is, the number ofdivided small areas. $\begin{matrix}{{h\left( {y,a} \right)} = {\frac{1}{I}{\sum\limits_{i = 0}^{J - 1}{s_{i}{\delta \left( {{y - y_{i}},{a - a_{i}}} \right)}}}}} & (9)\end{matrix}$

[0063] In this case, the quantity of translation y_(i) and the expansionrate a_(i) acquired from a section with high similarity are commonquantity translation y_(i) and expansion rate a_(i), and the similaritys_(i) acquired from the section with high similarity has a relativelylarge value, as indicated by the above-described equation (3).Therefore, a large peak is formed at a predetermined point by the votingoperation.

[0064] On the other hand, the quantity of translation y_(i) and theexpansion rate a_(i) acquired from a section with low similarity areaccidental and unsteady, and the similarity s_(i) acquired from thesection with low similarity has a relatively small value. Therefore, alarge peak is not formed because of the dispersion by the votingoperation.

[0065]FIG. 3 shows the result of voting in which this technique isapplied to the signals of FIGS. 1A and 1B. As can be seen from FIG. 3, alarge peak is formed at a point where the quantity of translation is 300samples and the expansion rate is 1.5, but no large peaks are formed inthe other parts.

[0066] After voting is done for all i (=0, 1, . . . I-1), the maximumpeak position is represented by (y_(m), a_(m)) and the integral value ofthe similarity s_(i) in this case is represented by s_(m), as expressedby the following equations (10) and (11). $\begin{matrix}{\left( {y_{m},a_{m}} \right) = {\underset{y,a}{\arg \quad \max}\quad {h\left( {y,a} \right)}}} & (10) \\{s_{m} = {{\max\limits_{y,a}\quad {h\left( {y,a} \right)}} = {h\left( {y_{m},a_{m}} \right)}}} & (11)\end{matrix}$

[0067] If the maximum similarity s_(m) does not exceed a predeterminedthreshold value s_(thsd), it is judged that the signals f(x) and g(x)are not similar to each other. On the contrary, if the maximumsimilarity s_(m) is equal to or higher than the threshold values_(thsd), it is judged that the signals are similar to each other orhave similar parts. The similarity between the two signals in this caseis the maximum similarity s_(m).

[0068] In this manner, in this embodiment, as local similarity is foundand the respective values of the local similarity are integrated, thesimilarity as a whole can be evaluated.

[0069] Moreover, by conversely finding a small area that is voted to thecell (y_(m), a_(m)), it is possible to find a signal section of f(x)similar to a signal section of g(x). Specifically, for example, thequantity of translation y_(i) and the expansion rate a_(i) at the timeof finding the similarity s_(i) for each sectional signal f_(i)(x) arestored and only the quantity of translation and expansion ratesufficiently close to the quantity of translation y_(m) and theexpansion rate a_(m) at the peak position are selected, therebydetecting a similar section.

[0070]FIG. 4 shows similar sections obtained as a result of theabove-described calculation. In FIG. 4, a value 1 indicates a sectionthat is judged to be similar and a value 0 indicates a section that isnot judged to be similar. Compared with the signal shown in FIG. 1A, thesections indicated by arrows in FIG. 1A have a value 1 and it can beconfirmed that the similar sections are detected.

[0071] In the above description, the expansion rate a=a_(i) and thequantity of translation y=y_(i) that minimize the noise energy J(a,y)with respect to the signal energy are found in equations (5) and (6).However, the present method is not limited to this and all the sectionsin which the noise energy J(a,y) with respect to the signal energy isequal to or less than a predetermined value may be voted.

[0072] In the above description, only the maximum similarity s_(m) ofthe similarity at the peak positions found by equations (10) and (11) iscompared with the threshold value s_(thsd) and if the threshold values_(thsd) is exceeded, the small area voted to the peak is converselyfound. However, the present method is not limited to this and the smallareas voted for all the peaks exceeding the threshold s_(thsd) may befound. Thus, if there are plural sections of the signal g(x) similar tosections of the signal f(x), all these sections can be extracted.

[0073] The principle of the similarity evaluation technique of thisembodiment is described above. Now, the schematic structure of a signalprocessing device of this embodiment will be described with reference toFIG. 5. In the following description, the signal processing device 10 isadapted for inputting a first signal and a second signal and evaluatingthe similarity between these signals. However, the signal processingdevice may input only one signal and evaluate the similarity betweendifferent sections of that signal.

[0074] As shown in FIG. 5, the signal processing device 10 of thisembodiment has an area dividing unit 11, a similarity calculating unit12, a voting unit 13, a similarity judging unit 14, and a similarsection detecting unit 15.

[0075] The area dividing unit 11 divides the first signal into smallareas. As described above, the number of divisions can be arbitrarilyset and small areas may overlap each other.

[0076] The similarity calculating unit 12 calculates the correlationbetween each of the small areas provided by division at the areadividing unit 11 and the second signal. The similarity calculating unit12 searches for the largest value of the acquired similarities, that is,the squares of correlation values, and acquires the similarity s, timedifference t and expansion rate a. The expansion rate a is amultiplication coefficient to realize a size of the pattern of the smallarea that is most coincident with the pattern of the second signal.

[0077] The voting unit 13 votes the acquired similarity s, timedifference t and expansion rate a into a voting space. The voting spaceis a feature space using the time difference t and the expansion rate aas variables for finding an integral value of the similarity s. Thesimilarity s is integrated at a position having the time difference tand the expansion rate a acquired from the small area.

[0078] As described above, when the first signal and the second signalinclude similar signal components, the patterns of the correspondingsmall areas are similar to each other. Therefore, the similarity sbetween these small areas is high and their time difference t andexpansion rate a are approximately coincident with those of the othersmall areas.

[0079] On the other hand, with respect to a small area corresponding toa part that is not similar, the maximum similarity is acquired at aposition that is accidentally most similar. Therefore, the overallsimilarity s is low and the time difference t and the expansion rate aare independent of those of the other small areas.

[0080] Therefore, when a similar signal component exists, voting ofplural small areas corresponding to this signal component concentratesat the same position and a significantly large peak is expected to beformed. When no similar component exists, the similarity is essentiallylow and voting is dispersed at different positions. Therefore, nosignificant peak is formed.

[0081] Thus, after voting for all the small areas is performed, thesimilarity judging unit 14 searches for the maximum similarity s_(m) inthe voting space and compares the maximum similarity s_(m) with thethreshold value s_(thsd), thereby judging the similarity.

[0082] When the similarity judging unit 14 judges that the similarity ishigh, the similar area detecting unit 15 detects a similar area. Thesimilar area detecting unit 15 detects a similar area, for example, byselecting only a small area where the time difference t and theexpansion rate a are sufficiently close to the time difference t_(m) andthe expansion rate a_(m) of the peak position.

[0083] The operation of the signal processing device 10 having theabove-described structure will now be described with reference to theflowchart of FIG. 6. First at step S10, the first signal is divided intosmall areas as described above, and at the next step S11, one of thesmall areas is selected.

[0084] At step S12, the correlation between the small area selected atstep S11 and the second signal is calculated.

[0085] At step S13, the largest value of the similarity obtained at stepS12 is found and the similarity s, the time difference t and theexpansion rate a are acquired.

[0086] At the next step S14, the similarity s, the time difference t andthe expansion rate a acquired at step S13 are voted in the voting space.That is, the similarity s is integrated at the position having the timedifference t and the expansion rate a acquired from the small area.

[0087] At step S15, whether processing is completed for all the smallareas or not is judged. If there still is a small area for whichprocessing is not completed at step S15, the processing returns to stepS11 and the above-described processing is repeated for the remainingsmall area. If processing is completed for all the small areas, theprocessing goes to step S16.

[0088] At step S16, the maximum similarity s_(m) in the voting space issearched for and acquired. At the next step S17, whether the maximumsimilarity s_(m) exceeds a predetermined threshold value s_(thsd) or notis judged. If the maximum similarity s_(m) does not exceed thepredetermined threshold value s_(thsd) (NO) at step S17, it is assumedthat no significant peak is formed and the processing goes to step S20.Then, it is judged that the first signal and the second signal are notsimilar to each other, and the processing ends. If the maximumsimilarity s_(m) exceeds the predetermined threshold value s_(thsd)(YES) at step S17, it is assumed that a significant peak is formed andthe processing goes to step S18.

[0089] At step S18, it is judged that the first signal and the secondsignal are similar to each other, and the time difference t_(m) and theexpansion rate a_(m) are acquired. The similarity between the firstsignal and the second signal is assumed to be the maximum similaritys_(m).

[0090] At step S19, similar areas are detected. Specifically, only smallareas having the time difference t and the expansion rate a that aresufficiently close to the time difference t_(m) and the expansion ratea_(m) of the peak position are selected, and the processing ends.

[0091] By carrying out the processing as described above, the signalprocessing device 10 can appropriately evaluate the similarity betweenobservation signals having unsteady noise and can extract similar partsfrom the signals.

[0092] The above-described signal processing device 10 can be used, forexample, in a coding device 20 as shown in FIG. 7. This coding device 20has the above-described signal processing device 10 and detects andseparately codes similar waveform parts of a signal in which a similarwaveform repeatedly appears, for example, an acoustic signal waveform,thereby improving the coding efficiency (compression efficiency).

[0093] As shown in FIG. 7, the coding device 20 has a section selectingunit 21, a similar section detecting unit 22, a similar componentsubtracting unit 23, a similar component coding unit 24, a signal codingunit 25, and an integrating unit 26. The similar section detecting unit22 is equivalent to the above-described signal processing device 10.

[0094] The operation of the coding device 20 having such a structurewill now be described with reference to the flowchart of FIG. 8 andFIGS. 9 to 12.

[0095] First at step S30, the section selecting unit 21 selects a firstsection PI₁ with an appropriate length, for example, one second, fromthe inputted first signal, as shown in FIG. 9. At the next step S31, thesection selecting unit 21 selects a second section PI₂ that is differentfrom the first section, for example, a section of 10 seconds followingthe first section PI₁.

[0096] At the next step S32, the similar section detecting unit 22detects whether the second section PI₂ has a section similar to thefirst section PI₁ or not. If the second has a similar section (YES) atstep S32, the processing goes to step S33. If the second section has nosimilar section, the processing goes to step S34.

[0097] At step S33, the similar section detecting unit 22 detects thestart time (T_(a), T₂) of the similar section, the expansion rate (a₂)and the length (L₂) of the similar section, as shown in FIG. 10, and thesimilar component coding unit 24 codes these. The processing thenreturns to step S32 and another similar section is detected. FIG. 10shows an example in which two more parts (T_(b), T₃, a₃, L₃) and (T_(c),T₄, a₄, L₄) are detected.

[0098] After all the similar sections are detected and coded, at stepS34, the similar component subtracting unit 23 subtracts the sectionssimilar to the first section PI₁ from the second section PI₂, as shownin FIG. 11. In the subtraction, the size is matched using the detectedexpansion rates. After the subtraction, non-similar components remain ineach similar section.

[0099] At step S35, the signal coding unit 25 codes the signal of thefirst section PI₁ using a typical signal coding method (for example,subband coding, transform coding or the like).

[0100] At step S36, the integrating unit 26 integrates the informationsuch as the start time coded at step S33 and the signal of the firstsection PI₁ coded at step S35 into one coded signal and outputs thecoded signal. Then, a first section PI₁ is newly taken as shown in FIG.12 and the processing is repeated. A shaded part in FIG. 12 indicates acoded part.

[0101]FIG. 13 shows an example of the integrated coded signal. As seenfrom FIG. 13, for example, the code the signal of the first section PI₁is followed by the codes obtained by coding the start time, expansionrate and length of the sections in the second section PI₂ similar to thefirst section PI₁. Next to this, the codes of the next section arearrayed.

[0102] With this coding device 20, since the typical coding method isused as it is in the first section, the coding efficiency is the same asthat of the typical coding method. However, in the second and subsequentsections, as components similar to the signal of the first section havebeen subtracted, the quantity of information of the signal is reducedand the coding efficiency can be improved, compared with the case ofusing the ordinary coding method as it is.

[0103] As described above, in the signal processing device of thisembodiment, a signal is divided into small areas and the similarity ofthe respective small areas is found and integrated to evaluate theoverall similarity. Therefore, the similarity can be evaluated even witha signal having unsteady noise that cannot be easily detected by atypical correlative method or the like.

[0104] Moreover, as this signal processing device is provided in acoding device to detect and separately code similar waveform parts inadvance with respect to a signal in which a similar waveform repeatedlyappears, the coding efficiency (compression efficiency) can be improved.

[0105] The signal processing method of this embodiment can also beapplied to detection of similar signals in the entire field of signalprocessing such as signal detection, acoustic processing, imageprocessing, and radar technology.

[0106] The present invention is not limited to the above-describedembodiment and various modifications can be made without departing fromthe scope of the invention.

[0107] For example, though the most common techniques of expansiveconversion and shift conversion are carried out and the expansion rateand the quantity of translation are used as conversion parameters in theabove description, the present invention is not limited to this and canbe applied to any conversion including nonlinear conversion.

[0108] Moreover, while the technique of minimizing the secondary errorenergy, which is the most common similarity evaluation quantity, isused, that is, the correlative method is used in the above description,the present invention is not limited to this and can be applied to othertypes of similarity evaluation quantity.

INDUSTRIAL APPLICABILITY

[0109] According to the present invention, a signal is divided intosmall areas and the similarity of the respective small areas is foundand integrated to evaluate the overall similarity. Therefore, thesimilarity can be evaluated even with a signal having unsteady noisethat cannot be easily detected by a typical correlative method or thelike. Moreover, as the present invention is used to detect andseparately code similar waveform parts in advance with respect to asignal in which a similar waveform repeatedly appears, the codingefficiency (compression efficiency) can be improved.

1. A signal processing method comprising: a division step of inputtingplural signals and dividing at least one of the plural signals intoplural small areas; a parameter extraction step of extracting aconversion parameter used for converting the small areas to acquiresimilarity with the other signal; a totaling step of totaling valuesindicating the degree of similarity found on the basis of the conversionparameter; and a similarity evaluation step of evaluating the similaritybetween the plural signals on the basis of the result of the totaling.2. The signal processing method as claimed in claim 1, furthercomprising a similar section extraction step of extracting a similarsection of the plural signals.
 3. The signal processing method asclaimed in claim 2, wherein at the similar section extraction step, thesmall area is extracted from which a conversion parameter substantiallyequal to the conversion parameter of a point where the result of thetotaling reaches its maximum is extracted.
 4. The signal processingmethod as claimed in claim 1, wherein the conversion is expansion and/orshift conversion.
 5. The signal processing method as claimed in claim 4,wherein the conversion parameter is found using a correlative method. 6.The signal processing method as claimed in claim 5, wherein theconversion parameter is an expansion rate and/or magnitude of a shift ata point where a maximum correlation value between the small area and theother signal is obtained.
 7. The signal processing method as claimed inclaim 1, wherein at the totaling step, values indicating the degree ofsimilarity between the plural signals are totaled in a space centeringthe conversion parameter as an axis.
 8. The signal processing method asclaimed in claim 1, wherein the values indicating the degree ofsimilarity at the totaling step are values proportional to thesimilarity between the plural signals.
 9. The signal processing methodas claimed in claim 8, wherein a correlation value between the pluralsignals or the square of the correlation value is used as thesimilarity.
 10. The signal processing method as claimed in claim 1,wherein the plural signals are different parts of one signal.
 11. Asignal processing method comprising: a division step of inputting pluralsignals and dividing at least one of the plural signals into pluralsmall areas; a parameter extraction step of extracting a conversionparameter used for converting the small areas to acquire similarity withthe other signal; a totaling step of totaling values indicating thedegree of similarity found on the basis of the conversion parameter; asimilarity evaluation step of evaluating the similarity between theplural signals on the basis of the result of the totaling; a similarsection extraction step of extracting a similar section of the pluralsignals; a first coding step of coding the similar section of the pluralsignals extracted at the similar section extraction step; and a secondcoding step of coding the sections other than the similar section. 12.The signal processing method as claimed in claim 11, wherein theconversion at the parameter extraction step is expansion and/or shiftconversion, and at the first coding step, information of start time ofthe similar section, expansion rate, and length of the similar sectionis coded.
 13. A signal processing device comprising: division means forinputting plural signals and dividing at least one of the plural signalsinto plural small areas; parameter extraction means for extracting aconversion parameter used for converting the small areas to acquiresimilarity with the other signal; totaling means for totaling valuesindicating the degree of similarity found on the basis of the conversionparameter; and similarity evaluation means for evaluating the similaritybetween the plural signals on the basis of the result of the totaling.14. The signal processing device as claimed in claim 13, furthercomprising similar section extraction means for extracting a similarsection of the plural signals.
 15. The signal processing device asclaimed in claim 14, wherein the similar section extraction meansextracts the small area from which a conversion parameter substantiallyequal to the conversion parameter of a point where the result of thetotaling reaches its maximum is extracted.
 16. The signal processingdevice as claimed in claim 13, wherein the conversion is expansionand/or shift conversion.
 17. The signal processing device as claimed inclaim 16, wherein the conversion parameter is found using a correlativemethod.
 18. The signal processing device as claimed in claim 17, whereinthe conversion parameter is an expansion rate and/or magnitude of ashift at a point where a maximum correlation value between the smallarea and the other signal is obtained.
 19. The signal processing deviceas claimed in claim 13, wherein the totaling means totals valuesindicating the degree of similarity between the plural signals in aspace centering the conversion parameter as an axis.
 20. The signalprocessing device as claimed in claim 13, wherein the values indicatingthe degree of similarity are values proportional to the similaritybetween the plural signals.
 21. The signal processing device as claimedin claim 20, wherein a correlation value between the plural signals orthe square of the correlation value is used as the similarity.
 22. Thesignal processing device as claimed in claim 13, wherein the pluralsignals are different parts of one signal.
 23. A signal processingdevice comprising: division means for inputting plural signals anddividing at least one of the plural signals into plural small areas;parameter extraction means for extracting a conversion parameter usedfor converting the small areas to acquire similarity with the othersignal; totaling means for totaling values indicating the degree ofsimilarity found on the basis of the conversion parameter; similarityevaluation means for evaluating the similarity between the pluralsignals on the basis of the result of the totaling; similar sectionextraction means for extracting a similar section of the plural signals;first coding means for coding the similar section of the plural signalsextracted by the similar section extraction means; and second codingmeans for coding the sections other than the similar section.
 24. Thesignal processing device as claimed in claim 23, wherein the conversionis expansion and/or shift conversion, and the first coding means codesinformation of start time of the similar section, expansion rate, andlength of the similar section.
 25. A signal processing programcomprising: a division step of inputting plural signals and dividing atleast one of the plural signals into plural small areas; a parameterextraction step of extracting a conversion parameter used for convertingthe small areas to acquire similarity with the other signal; a totalingstep of totaling values indicating the degree of similarity found on thebasis of the conversion parameter; and a similarity evaluation step ofevaluating the similarity between the plural signals on the basis of theresult of the totaling.
 26. The signal processing program as claimed inclaim 25, further comprising a similar section extraction step ofextracting a similar section of the plural signals.
 27. The signalprocessing program as claimed in claim 26, wherein at the similarsection extraction step, the small area is extracted from which aconversion parameter substantially equal to the conversion parameter ofa point where the result of the totaling reaches its maximum isextracted.
 28. The signal processing program as claimed in claim 25,wherein the conversion is expansion and/or shift conversion.
 29. Thesignal processing program as claimed in claim 28, wherein the conversionparameter is found using a correlative method.
 30. The signal processingprogram as claimed in claim 29, wherein the conversion parameter is anexpansion rate and/or magnitude of a shift at a point where a maximumcorrelation value between the small area and the other signal isobtained.
 31. The signal processing program as claimed in claim 25,wherein at the totaling step, values indicating the degree of similaritybetween the plural signals are totaled in a space centering theconversion parameter as an axis.
 32. The signal processing program asclaimed in claim 25, wherein the values indicating the degree ofsimilarity at the totaling step are values proportional to thesimilarity between the plural signals.
 33. The signal processing programas claimed in claim 32, wherein a correlation value between the pluralsignals or the square of the correlation value is used as thesimilarity.
 34. The signal processing program as claimed in claim 25,wherein the plural signals are different parts of one signal.
 35. Asignal processing program comprising: a division step of inputtingplural signals and dividing at least one of the plural signals intoplural small areas; a parameter extraction step of extracting aconversion parameter used for converting the small areas to acquiresimilarity with the other signal; a totaling step of totaling valuesindicating the degree of similarity found on the basis of the conversionparameter; a similarity evaluation step of evaluating the similaritybetween the plural signals on the basis of the result of the totaling; asimilar section extraction step of extracting a similar section of theplural signals; a first coding step of coding the similar section of theplural signals extracted at the similar section extraction step; and asecond coding step of coding the sections other than the similarsection.
 36. The signal processing program as claimed in claim 35,wherein the conversion is expansion and/or shift conversion, and at thefirst coding step, information of start time of the similar section,expansion rate, and length of the similar section is coded.
 37. Acomputer-controllable recording medium having a signal processingprogram recorded thereon, the signal processing program comprising: adivision step of inputting plural signals and dividing at least one ofthe plural signals into plural small areas; a parameter extraction stepof extracting a conversion parameter used for converting the small areasto acquire similarity with the other signal; a totaling step of totalingvalues indicating the degree of similarity found on the basis of theconversion parameter; and a similarity evaluation step of evaluating thesimilarity between the plural signals on the basis of the result of thetotaling.
 38. The recording medium as claimed in claim 37, having thesignal processing program recorded thereon, the signal processingprogram further comprising a similar section extraction step ofextracting a similar section of the plural signals.
 39. The recordingmedium as claimed in claim 38, having the signal processing programrecorded thereon wherein at the similar section extraction step, thesmall area is extracted from which a conversion parameter substantiallyequal to the conversion parameter of a point where the result of thetotaling reaches its maximum is extracted.
 40. The recording medium asclaimed in claim 37, having the signal processing program recordedthereon wherein the conversion is expansion and/or shift conversion. 41.The recording medium as claimed in claim 40, having the signalprocessing program recorded thereon wherein the conversion parameter isfound using a correlative method.
 42. The recording medium as claimed inclaim 41, having the signal processing program recorded thereon whereinthe conversion parameter is an expansion rate and/or magnitude of ashift at a point where a maximum correlation value between the smallarea and the other signal is obtained.
 43. The recording medium asclaimed in claim 37, having the signal processing program recordedthereon wherein at the totaling step, values indicating the degree ofsimilarity between the plural signals are totaled in a space centeringthe conversion parameter as an axis.
 44. The recording medium as claimedin claim 37, having the signal processing program recorded thereonwherein the values indicating the degree of similarity at the totalingstep are values proportional to the similarity between the pluralsignals.
 45. The recording medium as claimed in claim 44, having thesignal processing program recorded thereon wherein a correlation valuebetween the plural signals or the square of the correlation value isused as the similarity.
 46. The recording medium as claimed in claim 37,having the signal processing program recorded thereon wherein the pluralsignals are different parts of one signal.
 47. A computer-controllablerecording medium having a signal processing program recorded thereon,the signal processing program comprising: a division step of inputtingplural signals and dividing at least one of the plural signals intoplural small areas; a parameter extraction step of extracting aconversion parameter used for converting the small areas to acquiresimilarity with the other signal; a totaling step of totaling valuesindicating the degree of similarity found on the basis of the conversionparameter; a similarity evaluation step of evaluating the similaritybetween the plural signals on the basis of the result of the totaling; asimilar section extraction step of extracting a similar section of theplural signals; a first coding step of coding the similar section of theplural signals extracted at the similar section extraction step; and asecond coding step of coding the sections other than the similarsection.
 48. The recording medium as claimed in claim 47, having thesignal processing program recorded thereon wherein the conversion isexpansion and/or shift conversion, and at the first coding step,information of start time of the similar section, expansion rate, andlength of the similar section is coded.